




'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCAmelCase_					=   {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCAmelCase_					=   logging.get_logger(__name__)





class  __lowercase    (	__magic_name__			):
   _a    						=   """maskformer"""
   _a    						=   {"""hidden_size""": """mask_feature_size"""}
   _a    						=   ["""resnet""", """swin"""]
   _a    						=   ["""detr"""]







   def __init__(						self   ,				UpperCamelCase = 256   ,				UpperCamelCase = 256   ,				UpperCamelCase = 0.1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = 0.02   ,				UpperCamelCase = 1.0   ,				UpperCamelCase = 1.0   ,				UpperCamelCase = 1.0   ,				UpperCamelCase = 20.0   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     Any:
     if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       __a								=       SwinConfig(
           image_size=384   ,				in_channels=3   ,				patch_size=4   ,				embed_dim=128   ,				depths=[2, 2, 18, 2]   ,				num_heads=[4, 8, 16, 32]   ,				window_size=12   ,				drop_path_rate=0.3   ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,				)

     if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
       __a								=       backbone_config.pop('model_type'   )
       __a								=       CONFIG_MAPPING[backbone_model_type]
       __a								=       config_class.from_dict(UpperCamelCase   )

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
           f"Supported model types: {','.join(self.backbones_supported   )}"   )

     if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       __a								=       DetrConfig()
     else:
       # verify that the decoder is supported
       __a								=       (
           decoder_config.pop('model_type'   ) if isinstance(UpperCamelCase   ,				UpperCamelCase   ) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
         raise ValueError(
             f"Transformer Decoder {decoder_type} not supported, please use one of"
             f" {','.join(self.decoders_supported   )}"   )
       if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       CONFIG_MAPPING[decoder_type]
         __a								=       config_class.from_dict(UpperCamelCase   )

     __a								=       backbone_config
     __a								=       decoder_config
     # main feature dimension for the model
     __a								=       fpn_feature_size
     __a								=       mask_feature_size
     # initializer
     __a								=       init_std
     __a								=       init_xavier_std
     # Hungarian matcher && loss
     __a								=       cross_entropy_weight
     __a								=       dice_weight
     __a								=       mask_weight
     __a								=       use_auxiliary_loss
     __a								=       no_object_weight
     __a								=       output_auxiliary_logits

     __a								=       self.decoder_config.encoder_attention_heads
     __a								=       self.decoder_config.num_hidden_layers
     super().__init__(**UpperCamelCase   )







   @classmethod
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     Optional[Any]:
     return cls(
         backbone_config=UpperCamelCase   ,				decoder_config=UpperCamelCase   ,				**UpperCamelCase   ,				)







   def 		UpperCamelCase__      (						self   )					->     Dict[str, any]:
     __a								=       copy.deepcopy(self.__dict__   )
     __a								=       self.backbone_config.to_dict()
     __a								=       self.decoder_config.to_dict()
     __a								=       self.__class__.model_type
     return output









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



